The reported work demonstrates the design and realization of an integrated mid-band (sub-6 GHz) and mmWave multiple input, multiple output (MIMO) antenna for 5G handheld devices. The proposed prototype consists of the two-port MIMO configuration of the mid-band antenna placed at the top and bottom of the substrate, while the 4-port mmWave MIMO antenna is placed sideways. The MIMO configuration at the top and bottom consists of a two-element array to achieve high gain at the mid-band spectrum, while the antennas placed sideways are optimized to cover the 5G-mmWave band spectrum. The overall dimensions of the board were selected the same as the of smartphones, i.e., 151 mm × 72 mm. The mid-band antenna has an operational bandwidth of 2.73 GHz, whereas the mmWave antenna has an impedance bandwidth of 3.85 GHz with a peak gain of 5.29 and 8.57 dBi, respectively. Furthermore, the design is analyzed for the various MIMO performance parameters; it was found that the proposed antennas offer high performance in terms of envelop correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG) and channel capacity loss (CCL) within operational range. A fabricated prototype was tested and measured results show strong agreement with predicted results. Moreover, the proposed work is compared with state-of-the-art work for the same applications to demonstrate its potential for targeted application.
Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without the loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman's correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI.
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